The arms race between attacks and defenses for machine learning models has come to a
forefront in recent years, in both the security community and the privacy community …

In recent years, the research community has increasingly focused on understanding the
security and privacy challenges posed by deep learning models. However, the security …

Machine learning models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

To promote secure and private artificial intelligence (SPAI), we review studies on the model
security and data privacy of DNNs. Model security allows system to behave as intended …

Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality
of ML models becomes paramount for two reasons:(a) a model can be a business …

Over the past few years, providers such as Google, Microsoft, and Amazon have started to
provide customers with access to software interfaces allowing them to easily embed …

As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of
performance in many critical applications, their vulnerability to attacks is still an open …

Data is the key factor to drive the development of machine learning (ML) during the past
decade. However, high-quality data, in particular labeled data, is often hard and expensive …